class_docstr: Wide sampling node-centric sampling strategy which gives priority to
  walks containing edges with the highest degree of predicates and
  objects. The degree of a predicate and an object being defined by the
  number of predicates and objects present in its neighborhood, but also by
  their number of occurrence in a Knowledge Graph.
  
  Attributes:
      _is_support_remote: True if the sampling strategy can be used with a
          remote Knowledge Graph, False Otherwise
          Defaults to False.
      _random_state: The random state to use to keep random determinism with
          the sampling strategy.
          Defaults to None.
      _vertices_deg: The degree of the vertices.
          Defaults to {}.
      _visited: Tags vertices that appear at the max depth or of which all
          their children are tagged.
          Defaults to set.
      inverse: True if the inverse algorithm must be used, False otherwise.
          Defaults to False.
      split: True if the split algorithm must be used, False otherwise.
          Defaults to False.
docstr: Gets the weight of a hop in the Knowledge Graph.
  
  Args:
      hop: The hop of a vertex in a (predicate, object) form to get the
          weight.
  
  Returns:
      The weight of a given hop.
  
  Raises:
      ValueError: If there is an attempt to access the weight of a hop
          without the sampling strategy having been trained.
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code: def get_weight(self, hop: Hop) -> float:
      """Gets the weight of a hop in the Knowledge Graph.
  
      Args:
          hop: The hop of a vertex in a (predicate, object) form to get the
              weight.
  
      Returns:
          The weight of a given hop.
  
      Raises:
          ValueError: If there is an attempt to access the weight of a hop
              without the sampling strategy having been trained.
  
      """
      if not (
          self._pred_degs or self._obj_degs or self._neighbor_counts
      ):
          raise ValueError(
              "You must call the `fit(kg)` method before get the weight of"
              + " a hop."
          )
      return (
          self._neighbor_counts[hop[0].name]
          + self._neighbor_counts[hop[1].name]
      ) * (
          (self._pred_degs[hop[0].name] + self._obj_degs[hop[1].name])
          / 2
      )
  
file_path: pyRDF2Vec/pyrdf2vec/samplers/wide.py
imports: from collections import defaultdict
  from typing import DefaultDict
  import attr
  from pyrdf2vec.graphs import KG
  from pyrdf2vec.samplers import Sampler
  from pyrdf2vec.typings import Hop
label_desc: Use image node_id 2 for calling the WideSampler obj's underlying member method code with example usage: obj.get_weight(hop) and returns: unknown